The importance of peptide detectability for protein identification, quantification, and experiment design in MS/MS proteomics

Journal of Proteome Research

Volumn 9, Issue 12, 2010, Pages 6288-6297

  Li, Yong Fuga ^a   Arnold, Randy J ^b   Tang, Haixu ^a   Radivojac, Predrag ^a  

^a INDIANA UNIVERSITY   (United States)

^b INDIANA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; DEINOCOCCUS RADIODURANS; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; NONHUMAN; PEPTIDE ANALYSIS; PRIORITY JOURNAL; PROTEOMICS; QUANTITATIVE ANALYSIS; REPRODUCIBILITY; THEORETICAL MODEL;

ALGORITHMS; BACTERIAL PROTEINS; CHROMATOGRAPHY, LIQUID; DEINOCOCCUS; NEURAL NETWORKS (COMPUTER); PEPTIDES; PROTEINS; PROTEOMICS; REPRODUCIBILITY OF RESULTS; TANDEM MASS SPECTROMETRY;

BACTERIA (MICROORGANISMS); DEINOCOCCUS RADIODURANS;

EID: 78649823422     PISSN: 15353893     EISSN: 15353907     Source Type: Journal    
DOI: 10.1021/pr1005586     Document Type: Article

References (29)

1. Tang, H.; Arnold, R. J.; Alves, P.; Xun, Z.; Clemmer, D. E.; Novotny, M. V.; Reilly, J. P.; Radivojac, P. A computational approach toward label-free protein quantification using predicted peptide detectability Bioinformatics 2006, 22 e481-e488
2. Alves, P.; Arnold, R. J.; Novotny, M. V.; Radivojac, P.; Reilly, J. P.; Tang, H. Advancement in protein inference from shotgun proteomics using peptide detectability Pac. Symp. Biocomput. 2007, 409-420
3. Li, Y. F.; Arnold, R. J.; Li, Y.; Radivojac, P.; Sheng, Q.; Tang, H. A Bayesian approach to protein inference problem in shotgun proteomics Res. Comput. Mol. Biol. (RECOMB'08), LNCS 2008, 4955, 167-180
4. Li, Y. F.; Arnold, R. J.; Li, Y.; Radivojac, P.; Sheng, Q.; Tang, H. A Bayesian approach to protein inference problem in shotgun proteomics J. Comput. Biol. 2009, 16, 1183-1193
5. Lu, P.; Vogel, C.; Wang, R.; Yao, X.; Marcotte, E. M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation Nature Biotechnol. 2007, 25, 117-124
6. Le Bihan, T.; Robinson, M.; Stewart, I.; Figeyss, D. Definition and characterization of a "trypsinosome" from specific peptide characteristics by nano-HPLC-MS/MS and in silico analysis of complex protein mixtures J. Proteome Res. 2004, 3, 1138-1148
7. Mallick, P.; Schirle, M.; Chen, S. S.; Flory, M. R.; Lee, H.; Martin, D.; Ranish, J.; Raught, B.; Schmitt, R.; Werner, T.; Kuster, B.; Aebersold, R. Computational prediction of proteotypic peptides for quantitative proteomics Nature Biotechnol. 2007, 25, 125-131
8. Wedge, D. C.; Gaskell, S. J.; Hubbard, S. J.; Kell, D. B.; Lau, K. W.; Eyers, C. Peptide detectability following ESI mass spectrometry: prediction using genetic programming. GECCO '07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, New York, NY, 2007; pp 2219 - 2225.
9. Sanders, W.; Bridges, S.; McCarthy, F.; Nanduri, B.; Burgess, S. Prediction of peptides observable by mass spectrometry applied at the experimental set level BMC Bioinf. 2007, 8 (Suppl 7) S23
10. Vogel, C.; Marcotte, E. Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data Nature Protoc. 2008, 3, 1444-1451
11. Webb-Robertson, B.-J. M.; Cannon, W. R.; Oehmen, C. S.; Shah, A. R.; Gurumoorthi, V.; Lipton, M. S.; Waters, K. M. A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics Bioinformatics 2008, 24, 1503-1509
12. Anderson, N. L.; Anderson, N. G. The human plasma proteome: history, character, and diagnostic prospects Mol. Cell. Proteomics 2002, 1, 845-867
13. Tabb, D. L.; Vega-Montoto, L.; Rudnick, P. A.; Variyath, A. M.; Ham, A.-J. L.; Bunk, D. M.; Kilpatrick, L. E.; Billheimer, D. D.; Blackman, R. K.; Cardasis, H. L.; Carr, S. A.; Clauser, K. R.; Jaffe, J. D.; Kowalski, K. A.; Neubert, T. A.; Regnier, F. E.; Schilling, B.; Tegeler, T. J.; Wang, M.; Wang, P.; Whiteaker, J. R.; Zimmerman, L. J.; Fisher, S. J.; Gibson, B. W.; Kinsinger, C. R.; Mesri, M.; Rodriguez, H.; Stein, S. E.; Tempst, P.; Paulovich, A. G.; Liebler, D. C.; Spiegelman, C. Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry J. Proteome Res. 2009, 9, 761-776
14. Keller, A.; Nesvizhskii, A. I.; Kolker, E.; Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search Anal. Chem. 2002, 74, 5383-5392
15. Lindgren, B. Statistical Theory; Chapman & Hall/CRC: Boca Raton, FL, 1993.
16. Mackay, D. J. C. Bayesian interpolation Neural Computation 1992, 4, 415-447
17. Perkins, D. N.; Pappin, D. J.; Creasy, D. M.; Cottrell, J. S. Probability-based protein identification by searching sequence databases using mass spectrometry data Electrophoresis 1999, 20, 3551-3567
18. Kuster, B.; Schirle, M.; Mallick, P.; Aebersold, R. Scoring proteomes with proteotypic peptide probes Nature Rev. Mol. Cell. Biol. 2005, 6, 577-583
19. Blonder, J.; Veenstra, T. Computational prediction of proteotypic peptides Expert Rev. Proteomics 2007, 4, 351-354
20. Friedman, J.; Tibshirani, R.; Hastie, T. The Elements of Statistical Learning: Data Mining, Inference and Prediction; Springer-Verlag: New York, 2001.
21. Mitzenmacher, M. A brief history of generative models for power law and lognormal distributions Internet Math. 2004, 1, 226-251
22. Bengtsson, M.; Ståhlberg, A.; Rorsman, P.; Kubista, M. Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels Genome Res. 2005, 15, 1388
23. Ghaemmaghami, S.; Huh, W.; Bower, K.; Howson, R.; Belle, A.; Dephoure, N.; O'Shea, E.; Weissman, J. Global analysis of protein expression in yeast Nature 2003, 425, 737-741 (Pubitemid 37314318)
24. Ueda, H. R.; Hayashi, S.; Matsuyama, S.; Yomo, T.; Hashimoto, S.; Kay, S. A.; Hogenesch, J. B.; Iino, M. Universality and flexibility in gene expression from bacteria to human Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 3765-3769
25. Lipton, M. S.; Paša-Tolić, L.; Anderson, G. A.; Anderson, D. J.; Auberry, D. L.; Battista, J. R.; Daly, M J.; Fredrickson, J.; Hixson, K. K.; Kostandarithes, H.; Masselon, C.; Markillie, L. M.; Moore, R. J.; Romine, M. F.; Shen, Y.; Stritmatter, E.; Tolić, N.; Udseth, H. R.; Venkateswaran, A.; Wong, K.-K.; Zhao, R.; Smith, R. D. Global analysis of the Deinococcus radiodurans proteome by using accurate mass tags Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 11049-11054
26. Picotti, P.; Bodenmiller, B.; Mueller, L.; Domon, B.; Aebersold, R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics Cell 2009, 138, 795-806
27. Magdon-Ismail, M.; Nicholson, A.; Abu-Mostafa, Y. Financial markets: very noisy information processing Proc. IEEE 1998, 86, 2184-2195
28. Alves, P.; Arnold, R. J.; Clemmer, D. E.; Li, Y.; Reilly, J. P.; Sheng, Q.; Tang, H.; Xun, Z.; Zeng, R.; Radivojac, P. Fast and accurate identification of semi-tryptic peptides in shotgun proteomics Bioinformatics 2008, 24, 102-109
29. Liu, H.; Sadygov, R.; Yates III, J. A model for random sampling and estimation of relative protein abundance in shotgun proteomics Anal. Chem. 2004, 76, 4193-4201